Recurrent neural network verifier for face detection and tracking

Authors:
Sung H. Yoon;Gi T. Hur;Jung H. Kim
Affiliations:
Computer Science Dept., North Carolina A&T State University, NC;Multimedia Contents Dept., DongShin University, South Korea;Electrical Engineering Dept., North Carolina A&T State University, NC
Venue:
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Year:
2006

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Abstract

This paper presents a new method for face verification for vision applications. There are many approaches to detect and track a face in a sequence of images; however, the high computations of image algorithms, as well as, face detection and head tracking failures under unrestricted environments remain to be a difficult problem. We present a robust algorithm that improves face detection and tracking in video sequences by using geometrical facial information and a recurrent neural network verifier. Two types of neural networks are proposed for face detection verification. A new method, a three-face reference model (TFRM), and its advantages, such as, allowing for a better match for face verification, will be discussed in this paper.